Name
Title | ||
|---|---|---|
Enhanced football game optimization-based K-means clustering for multi-level segmentation of medical images |
Abstract | ||
|---|---|---|
This paper presents a football game optimization (FGO)-based K-means clustering for multi-level segmentation of medical images. The FGO was developed by modelling the behaviour of players in randomly moving to better positions with a target of scoring a goal. It performs simple random walks independently and/or under the guidance of a coach. In fact, the players often perform long jumps to grab the ball while playing. Such jumping action was not considered in the existing FGO. This paper first proposes an enhanced FGO (EFGO) by modelling the jumping actions of players using Levy Flight mechanism. The EFGO is then combined with K-means clustering for performing multi-level segmentation of medical and other colour images. The results of six sample images clearly portray the superior performance of the proposed EFGO-based segmentation method. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s13748-021-00251-5 | PROGRESS IN ARTIFICIAL INTELLIGENCE |
Keywords | DocType | Volume |
Football game optimization, Segmentation, K-means clustering, Medical images | Journal | 10 |
Issue | ISSN | Citations |
4 | 2192-6352 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| T. K. Abhiraj | 1 | 0 | 0.34 |
| Koganti Srilakshmi | 2 | 0 | 1.01 |
| Kumaran Jayaraman | 3 | 0 | 0.34 |
| Sasikala Jayaraman | 4 | 0 | 0.34 |